A Hybrid Ensemble Model Combining Machine Learning and Fuzzy Logic for Robust Stress Level Detection
fuzzy logic; hybrid ensemble; machine learning; robustness; Stress detection; weighted voting
Accurate stress detection remains challenging because stress manifests differently across diverse populations. This research presents a novel hybrid approach that combines four machine learning classifiers - XGBoost, Random Forest, Support Vector Machine, and Logistic Regression - with an enhanced fuzzy logic system through weighted voting. The study employed a comprehensive dataset comprising over 1,100 samples containing more than 45 features that capture psychological, academic, social, physical, and environmental stress indicators. The results demonstrate that the ensemble achieves 87.27% accuracy with statistically significant improvements over individual models (p0.001). The approach maintains strong performance when 10% noise is injected or missing data is simulated, and provides well-calibrated probability estimates that could support clinical decision-making. 2025 IEEE.
Thakur, Utkarsh; Shanthan, Hubert; Arputharaj, Vijay
Proceedings of the 9th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2025;pp.1956-1963
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/ICECA66444.2025.11382770 https://www.scopus.com/pages/publications/105034886621?origin=resultslist
Thakur U., Christ (Deemed To Be University), Department of Computer Science, Central Campus, Karnataka, Bangalore, India; Shanthan H., Christ (Deemed To Be University), Department of Computer Science, Central Campus, Karnataka, Bangalore, India; Arputharaj V., Christ (Deemed To Be University), Department of Computer Science, Central Campus, Karnataka, Bangalore, India